stream-chain

What This Skill Does

The stream-chain skill for OpenClaw is a powerful orchestration engine designed to transform simple agent tasks into robust, multi-stage pipelines. By enabling sequential data flow where the output of one prompt serves as the context for the next, this skill allows users to automate complex cognitive workflows. Whether you are performing deep code analysis, data transformation, or multi-step content generation, stream-chain ensures that each subsequent step builds intelligently upon the previous results. It supports both fully custom prompt chains for unique requirements and optimized, predefined pipelines for common development and data tasks.

Installation

To integrate this skill into your OpenClaw environment, execute the following installation command in your terminal: clawhub install openclaw/skills/skills/adolago/stream-chain Ensure you have the latest version of the OpenClaw CLI to access all features, including timeout management and verbose debugging flags.

Use Cases

1. Advanced Code Refactoring: Instead of asking for a rewrite in one go, use stream-chain to identify smells, draft a plan, implement changes, and verify behavior sequentially.
2. Security Audits: Chain steps to scan code, categorize vulnerabilities by severity, suggest prioritized fixes, and generate automated test suites to validate those fixes.
3. Data Normalization: Build a pipeline that extracts raw data from various sources, transforms it into a standard schema, validates the output, and produces a summary report.
4. Content Lifecycle: Automate the creation of technical documentation by drafting, reviewing, formatting, and generating metadata in one fluid execution.

Example Prompts

1. "stream-chain run 'Analyze the performance bottlenecks in the database module' 'Propose three indexing strategies' 'Simulate the impact of each strategy on read-heavy workloads'"
2. "stream-chain run 'Summarize the user requirements for the new dashboard' 'Create a component architecture map' 'Draft the interface definitions'"
3. "stream-chain pipeline analysis"

Tips & Limitations

• Timeout Management: For long-running operations or large codebases, always increase the --timeout value to prevent premature termination of your chain.
• Sequential Context: Remember that later steps inherit all context from earlier steps. If your chain grows too long, tokens may be consumed quickly; keep prompts concise to manage context window limits effectively.
• Debugging: When building complex custom chains, utilize the --debug flag to inspect exactly how the output of one step is being transformed before entering the next.